{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "burgers-super-resolution.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q-mfxSBFN9Ur",
        "colab_type": "text"
      },
      "source": [
        "# Super-resolution of Burgers' equation\n",
        "\n",
        "This notebook reconstructs Figure 1 from our paper [Learning data-driven discretizations for partial differential equations](https://www.pnas.org/content/116/31/15344), training a model from scratch.\n",
        "\n",
        "This is useful for gaining insight about these methods (and our code), but it's worth reiterating that this model *not* one that we used for actual time-integration."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2iJfw6LssUtV",
        "colab_type": "text"
      },
      "source": [
        "### Copyright 2019 Google LLC"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s8ogRhxsslb-",
        "colab_type": "text"
      },
      "source": [
        "Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "you may not use this file except in compliance with the License.\n",
        "You may obtain a copy of the License at\n",
        "\n",
        "    https://www.apache.org/licenses/LICENSE-2.0\n",
        "\n",
        "Unless required by applicable law or agreed to in writing, software\n",
        "distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "See the License for the specific language governing permissions and\n",
        "limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bnFv_kvT-xCr",
        "colab_type": "text"
      },
      "source": [
        "## Setup environment"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ks-XlPLvrhd6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "! pip install -q -U xarray matplotlib\n",
        "! rm -rf data-driven-discretization-1d\n",
        "! git clone https://github.com/google/data-driven-discretization-1d.git\n",
        "! pip install -q -e data-driven-discretization-1d\n",
        "# install the seaborn bug-fix from https://github.com/mwaskom/seaborn/pull/1602\n",
        "! pip install -U -q git+git://github.com/stfnrpplngr/seaborn.git@309a9de383fac4db1c66dbf87815c4ba0c439c59\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VQ1cOIS5rvC5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Ensure we're using Tensorflow 1.x in Colab. If not using Colab, remove this magic.\n",
        "%tensorflow_version 1.x\n",
        "import tensorflow as tf\n",
        "assert tf.__version__[:2] == '1.'\n",
        "\n",
        "import seaborn\n",
        "assert seaborn.__version__ == '0.9.0', 'restart kernel after running previous cell'\n",
        "from matplotlib.colors import LogNorm\n",
        "\n",
        "import enum\n",
        "import numpy as np\n",
        "import matplotlib\n",
        "from matplotlib import pyplot as plt\n",
        "import sys, time, os, h5py\n",
        "\n",
        "import os\n",
        "import json\n",
        "import numpy as np\n",
        "import seaborn\n",
        "import pandas as pd\n",
        "import xarray\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import pde_superresolution.utils\n",
        "import pde_superresolution as pde\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C8sfdHR9RLOc",
        "colab_type": "text"
      },
      "source": [
        "## Library code"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OMvRpQ8021Kp",
        "colab_type": "text"
      },
      "source": [
        "### dataset generation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vdtx9Fen2zhh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "def _stack_all_rolls(inputs: tf.Tensor, max_offset: int) -> tf.Tensor:\n",
        "  \"\"\"Stack together all rolls of inputs, from 0 to max_offset.\"\"\"\n",
        "  rolled = [tf.concat([inputs[i:, ...], inputs[:i, ...]], axis=0)\n",
        "            for i in range(max_offset)]\n",
        "  return tf.stack(rolled, axis=0)\n",
        "\n",
        "\n",
        "@enum.unique\n",
        "class Dataset(enum.Enum):\n",
        "  TRAINING = 0\n",
        "  VALIDATION = 1\n",
        "\n",
        "\n",
        "def _model_inputs(fine_inputs, resample_factor):\n",
        "  inputs = fine_inputs[:, resample_factor-1::resample_factor]\n",
        "\n",
        "  labels = tf.stack([fine_inputs[:, offset-1::resample_factor]\n",
        "                     for offset in range(1, resample_factor)], axis=-1)\n",
        "\n",
        "  base_grid = pde.polynomials.regular_grid(\n",
        "      pde.polynomials.GridOffset.STAGGERED, derivative_order=0,\n",
        "      accuracy_order=hparams.coefficient_grid_min_size, dx=1)\n",
        "  baselines = []\n",
        "  for offset in range(1, hparams.resample_factor):\n",
        "    current_grid = base_grid + 0.5 - offset / hparams.resample_factor\n",
        "    method = pde.polynomials.Method.FINITE_DIFFERENCES\n",
        "    reconstruction = pde.polynomials.reconstruct(\n",
        "        inputs, current_grid, method, derivative_order=0)\n",
        "    baselines.append(reconstruction)\n",
        "  baseline = tf.stack(baselines, axis=-1)\n",
        "\n",
        "  results = {'inputs': inputs, 'labels': labels, 'baseline': baseline}\n",
        "\n",
        "  for accuracy_order in [1, 3, 5]:\n",
        "    base_grid = pde.polynomials.regular_grid(\n",
        "        pde.polynomials.GridOffset.STAGGERED, derivative_order=0,\n",
        "        accuracy_order=accuracy_order, dx=1)\n",
        "    baselines = []\n",
        "    for offset in range(1, hparams.resample_factor):\n",
        "      current_grid = base_grid + 0.5 - offset / hparams.resample_factor\n",
        "      method = pde.polynomials.Method.FINITE_DIFFERENCES\n",
        "      reconstruction = pde.polynomials.reconstruct(\n",
        "          inputs, current_grid, method, derivative_order=0)\n",
        "      baselines.append(reconstruction)\n",
        "    results[f'baseline_{accuracy_order}'] = tf.stack(baselines, axis=-1)\n",
        "\n",
        "  return results\n",
        "\n",
        "\n",
        "def make_dataset(snapshots,\n",
        "                 hparams,\n",
        "                 dataset_type: Dataset = Dataset.TRAINING,\n",
        "                 repeat: bool = True,\n",
        "                 evaluation: bool = False) -> tf.data.Dataset:\n",
        "  snapshots = np.asarray(snapshots, dtype=np.float32)\n",
        "\n",
        "  num_training = int(round(snapshots.shape[0] * hparams.frac_training))\n",
        "  if dataset_type is Dataset.TRAINING:\n",
        "    indexer = slice(None, num_training)\n",
        "  else:\n",
        "    assert dataset_type is Dataset.VALIDATION\n",
        "    indexer = slice(num_training, None)\n",
        "\n",
        "  dataset = tf.data.Dataset.from_tensor_slices(snapshots[indexer])\n",
        "  # no need to do dataset augmentation with rolling for eval\n",
        "  rolls_stop = 1 if evaluation else hparams.resample_factor\n",
        "  dataset = dataset.map(lambda x: _stack_all_rolls(x, rolls_stop))\n",
        "  dataset = dataset.map(lambda x: _model_inputs(x, hparams.resample_factor))\n",
        "  dataset = dataset.apply(tf.data.experimental.unbatch())\n",
        "  dataset = dataset.cache()\n",
        "\n",
        "  if repeat:\n",
        "    dataset = dataset.apply(\n",
        "        tf.data.experimental.shuffle_and_repeat(buffer_size=10000))\n",
        "\n",
        "  batch_size = hparams.base_batch_size * hparams.resample_factor\n",
        "  dataset = dataset.batch(batch_size)\n",
        "  dataset = dataset.prefetch(buffer_size=1)\n",
        "  return dataset\n",
        "\n",
        "\n",
        "def stack_reconstruction(inputs, predictions):\n",
        "  if isinstance(inputs, tf.Tensor):\n",
        "    stacked = tf.concat([predictions, inputs[..., tf.newaxis], ], axis=-1)\n",
        "    return tf.layers.flatten(stacked)\n",
        "  else:\n",
        "    stacked = np.concatenate([predictions, inputs[..., np.newaxis]], axis=-1)\n",
        "    new_shape = stacked.shape[:-2] + (np.prod(stacked.shape[-2:]),)\n",
        "    return stacked.reshape(new_shape)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OFail-w02uO5",
        "colab_type": "text"
      },
      "source": [
        "### model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rtbZrqa6rvPH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def predict_coefficients(inputs: tf.Tensor,\n",
        "                         hparams: tf.contrib.training.HParams,\n",
        "                         reuse: object = tf.AUTO_REUSE) -> tf.Tensor:\n",
        "  _, equation = pde.equations.from_hparams(hparams)\n",
        "  pde.model.assert_consistent_solution(equation, inputs)\n",
        "\n",
        "  with tf.variable_scope('predict_coefficients', reuse=reuse):\n",
        "    num_derivatives = len(equation.DERIVATIVE_ORDERS)\n",
        "\n",
        "    base_grid = pde.polynomials.regular_grid(\n",
        "        pde.polynomials.GridOffset.STAGGERED, derivative_order=0,\n",
        "        accuracy_order=hparams.coefficient_grid_min_size,\n",
        "        dx=1.0)\n",
        "\n",
        "    net = inputs[:, :, tf.newaxis]\n",
        "    net /= equation.standard_deviation\n",
        "\n",
        "    activation = pde.model._NONLINEARITIES[hparams.nonlinearity]\n",
        "\n",
        "    for _ in range(hparams.num_layers - 1):\n",
        "      net = pde.layers.conv1d_periodic_layer(net, filters=hparams.filter_size,\n",
        "                                         kernel_size=hparams.kernel_size,\n",
        "                                         activation=activation, center=True)\n",
        "\n",
        "    poly_accuracy_layers = []\n",
        "    for offset in range(1, hparams.resample_factor):\n",
        "      current_grid = base_grid + 0.5 - offset / hparams.resample_factor\n",
        "      method = pde.polynomials.Method.FINITE_DIFFERENCES\n",
        "      poly_accuracy_layers.append(\n",
        "          pde.polynomials.PolynomialAccuracyLayer(\n",
        "              grid=current_grid,\n",
        "              method=method,\n",
        "              derivative_order=0,\n",
        "              accuracy_order=hparams.polynomial_accuracy_order,\n",
        "              out_scale=hparams.polynomial_accuracy_scale)\n",
        "      )\n",
        "    input_sizes = [layer.input_size for layer in poly_accuracy_layers]\n",
        "\n",
        "    if hparams.num_layers > 0:\n",
        "      net = pde.layers.conv1d_periodic_layer(net, filters=sum(input_sizes),\n",
        "                                          kernel_size=hparams.kernel_size,\n",
        "                                          activation=None, center=True)\n",
        "    else:\n",
        "      initializer = tf.initializers.zeros()\n",
        "      coefficients = tf.get_variable(\n",
        "          'coefficients', (sum(input_sizes),),\n",
        "          initializer=initializer)\n",
        "      net = tf.tile(coefficients[tf.newaxis, tf.newaxis, :],\n",
        "                    [tf.shape(inputs)[0], inputs.shape[1].value, 1])\n",
        "\n",
        "    cum_sizes = np.cumsum(input_sizes)\n",
        "    starts = [0] + cum_sizes[:-1].tolist()\n",
        "    stops = cum_sizes.tolist()\n",
        "    zipped = zip(starts, stops, poly_accuracy_layers)\n",
        "\n",
        "    outputs = tf.stack([layer.apply(net[..., start:stop])\n",
        "                        for start, stop, layer in zipped], axis=-2)\n",
        "    assert outputs.shape.as_list()[-1] == base_grid.size\n",
        "\n",
        "    return outputs\n",
        "\n",
        "\n",
        "def predict(inputs, hparams):\n",
        "  coefficients = predict_coefficients(inputs, hparams)\n",
        "  return pde.model.apply_coefficients(coefficients, inputs)\n",
        "\n",
        "\n",
        "def setup_training(dataset, hparams, scale=1.0):\n",
        "  tensors = dataset.make_one_shot_iterator().get_next()\n",
        "  predictions = predict(tensors['inputs'], hparams)\n",
        "  loss = tf.reduce_mean((tensors['labels'] - predictions) ** 2) / scale\n",
        "  train_step = pde.training.create_training_step(loss, hparams)\n",
        "  return loss, train_step\n",
        "\n",
        "\n",
        "def baseline_loss(snapshots, hparams):\n",
        "  dataset = make_dataset(snapshots, hparams, repeat=False, evaluation=True)\n",
        "\n",
        "  tensors = dataset.make_one_shot_iterator().get_next()\n",
        "  loss = tf.reduce_mean((tensors['labels'] - tensors['baseline_1']) ** 2)\n",
        "\n",
        "  sess = tf.Session(config=pde.training._session_config())\n",
        "  losses = []\n",
        "  while True:\n",
        "    try:\n",
        "      losses.append(sess.run(loss))\n",
        "    except tf.errors.OutOfRangeError:\n",
        "      break\n",
        "  return np.mean(losses)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QOuepaOTRJ1G",
        "colab_type": "text"
      },
      "source": [
        "## one time step"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8txZ0dwsININ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "40bdedf7-c383-408c-8b6f-c2c0dc2d6489"
      },
      "source": [
        "! gsutil cp gs://data-driven-discretization-public/training-data/burgers.h5 ."
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Copying gs://data-driven-discretization-public/training-data/burgers.h5...\n",
            "- [1 files][ 39.1 MiB/ 39.1 MiB]                                                \n",
            "Operation completed over 1 objects/39.1 MiB.                                     \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bzjQj7tkrybY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "with h5py.File('burgers.h5') as f:\n",
        "  snapshots = f['v'][...]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IByBzCeCRX9w",
        "colab_type": "text"
      },
      "source": [
        "## visualize an example"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TVH9f7fKBlDM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "hparams = pde.training.create_hparams(\n",
        "    equation='burgers',\n",
        "    conservative=False,\n",
        "    coefficient_grid_min_size=6,\n",
        "    resample_factor=16,\n",
        "    equation_kwargs=json.dumps(dict(num_points=512)),\n",
        "    base_batch_size=32,\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GhgKB1U4JItK",
        "colab_type": "text"
      },
      "source": [
        "### Baseline performance"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zl8R5brBvkZ6",
        "colab_type": "code",
        "outputId": "97f91c14-1e9e-417a-dc5d-db55b5a16c93",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        }
      },
      "source": [
        "demo_dataset = make_dataset(snapshots, hparams, repeat=False, evaluation=True)\n",
        "\n",
        "sess = tf.Session(config=pde.training._session_config())\n",
        "tf_example = demo_dataset.make_one_shot_iterator().get_next()\n",
        "example = sess.run(tf_example)\n",
        "\n",
        "plt.figure(figsize=(16, 4))\n",
        "example_id = 2\n",
        "plt.scatter(np.arange(0, 512, hparams.resample_factor),\n",
        "            np.roll(example['inputs'][example_id], 1, axis=-1), marker='s')\n",
        "plt.plot(stack_reconstruction(example['inputs'], example['baseline'])[example_id], label='baseline')\n",
        "plt.plot(stack_reconstruction(example['inputs'], example['labels'])[example_id], label='exact')\n",
        "plt.legend()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-15-b10444f6db1f>:4: DatasetV1.make_one_shot_iterator (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `for ... in dataset:` to iterate over a dataset. If using `tf.estimator`, return the `Dataset` object directly from your input function. As a last resort, you can use `tf.compat.v1.data.make_one_shot_iterator(dataset)`.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f6fee5559b0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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TT6jW0TTXp2VNIoNNjPlzj+NAag/IXg/FedoGE25PtdtZ9s3rNPptAGW6IPL6zadF90Fa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\nQmCC\n",
            "text/plain": [
              "<Figure size 1152x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L4NnsS_BJLi_",
        "colab_type": "text"
      },
      "source": [
        "### Untrained model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_peIFkhVbh-F",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "b7b28f20-b6e1-433d-8ac4-496772e424bf"
      },
      "source": [
        "demo_dataset = make_dataset(snapshots, hparams, Dataset.VALIDATION, repeat=False, evaluation=True)\n",
        "\n",
        "tensors = demo_dataset.make_one_shot_iterator().get_next()\n",
        "tensors['predictions'] = predict(tensors['inputs'], hparams)\n",
        "sess.run(tf.global_variables_initializer())\n",
        "example = sess.run(tensors)\n",
        "\n",
        "for example_id in [0, 10, 20, 30, 40]:\n",
        "  plt.figure(figsize=(16, 4))\n",
        "  plt.scatter(np.arange(0, 512, hparams.resample_factor),\n",
        "              np.roll(example['inputs'][example_id], 1, axis=-1), marker='s')\n",
        "  plt.plot(stack_reconstruction(example['inputs'], example['baseline'])[example_id], label='baseline')\n",
        "  plt.plot(stack_reconstruction(example['inputs'], example['labels'])[example_id], label='exact')\n",
        "  plt.plot(stack_reconstruction(example['inputs'], example['predictions'])[example_id], label='predictions')\n",
        "  plt.legend()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kmGScfic6cx0D4gYw+5ZP0GNgiXkUtUEH6cEgAPaABWPAGt0/HEgmHEgA4L5P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\n",
            "text/plain": [
              "<Figure size 1152x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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KOdI1qi2VnYaEgqh4czwJloRGgVlTyp3aes2d1TuJNcfSLbobfeP7srZoLb6AD6PeSFVZ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4PNXP3m1eyo3dHp8+FomNf2vEYwEqSsqYwMs5esV64kP2iixOVml7WGC1+6kJ+v+DlWk1WW+REd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\nhRBCCCGEEEIMKRKoCiGEEEIIIYQYUv4/7gzAKNMxnbYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1152x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6lY3/t48f6v07a03jL06I33C7G19xvGPHjsyfP/+k+/w2UP3hhx8IDg5u9Lla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\nQgghRI8iiaoQQgghhBBCiB7l/wFbHcTn0nUBQAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1152x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Pt7a/xbKblrG5eDMA6wrWUeIsYXDMRewo28XsjW8RSWCO+ye/+Rk9oo4OHWwY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\nhBBCCCFElyKJqhBCCCGEEEKILuX/AS2ZIoFxi5JTAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1152x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ZJEUkkRiRSFANYjVYSY5IbrHPUOnvugotUO0T3QeAYalJKKqR9SX7mJU/ixu/urHdNYbD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34bO4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\n2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VNYetVSdGpdi49JEJrV0RnVo2TGWwn5lefOpPPv6WZUfUEfdcG6cKAaY4xplFF9c90m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\nqVdsVCVJkiRJvWKjKkmSJEnqlfcBs6Ur04F6zeYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1152x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wm2Bu_P6RcIz",
        "colab_type": "text"
      },
      "source": [
        "## train a model (optional)\n",
        "\n",
        "Note that below we'll download the saved results from a previous run."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-dvBN_u1MI0X",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "hparams = pde.training.create_hparams(\n",
        "    equation='burgers',\n",
        "    conservative=False,\n",
        "    coefficient_grid_min_size=6,\n",
        "    resample_factor=8,\n",
        "    equation_kwargs=json.dumps(dict(num_points=512)),\n",
        "    eval_interval=500,\n",
        "    learning_stops=[20000, 40000],\n",
        "    learning_rates=[3e-3, 3e-4],\n",
        ")\n",
        "loss_scale = baseline_loss(snapshots, hparams)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W2HjZwrUJXpf",
        "colab_type": "code",
        "outputId": "365bed22-6689-4998-e647-97f5b71a76c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        }
      },
      "source": [
        "%%time\n",
        "tf.reset_default_graph()\n",
        "dataset = make_dataset(snapshots, hparams)\n",
        "loss, train_step = setup_training(dataset, hparams, scale=loss_scale)\n",
        "\n",
        "sess = tf.Session(config=pde.training._session_config())\n",
        "sess.run(tf.global_variables_initializer())"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-3-74b9ee882d14>:75: shuffle_and_repeat (from tensorflow.python.data.experimental.ops.shuffle_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.data.Dataset.shuffle(buffer_size, seed)` followed by `tf.data.Dataset.repeat(count)`. Static tf.data optimizations will take care of using the fused implementation.\n",
            "WARNING:tensorflow:From /content/data-driven-discretization-1d/pde_superresolution/training.py:203: The name tf.train.get_or_create_global_step is deprecated. Please use tf.compat.v1.train.get_or_create_global_step instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/data-driven-discretization-1d/pde_superresolution/training.py:206: The name tf.train.piecewise_constant is deprecated. Please use tf.compat.v1.train.piecewise_constant instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/data-driven-discretization-1d/pde_superresolution/training.py:212: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/data-driven-discretization-1d/pde_superresolution/training.py:214: The name tf.GraphKeys is deprecated. Please use tf.compat.v1.GraphKeys instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/math_grad.py:1375: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "CPU times: user 1.23 s, sys: 58.6 ms, total: 1.29 s\n",
            "Wall time: 1.31 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t9aaxP0KPnRK",
        "colab_type": "code",
        "outputId": "8019ab5f-7cbe-4e67-c35c-6d578445d32b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "%%time\n",
        "for step in range(hparams.learning_stops[-1]):\n",
        "  sess.run(train_step)\n",
        "  if (step + 1) % hparams.eval_interval == 0:\n",
        "    print(step, sess.run(loss))\n"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "499 0.035062023\n",
            "999 0.02400635\n",
            "1499 0.025569342\n",
            "1999 0.016453508\n",
            "2499 0.017697832\n",
            "2999 0.009676907\n",
            "3499 0.011365009\n",
            "3999 0.0092496555\n",
            "4499 0.014312735\n",
            "4999 0.009259289\n",
            "5499 0.011016512\n",
            "5999 0.007732385\n",
            "6499 0.008062332\n",
            "6999 0.013416935\n",
            "7499 0.0055208383\n",
            "7999 0.0094934385\n",
            "8499 0.006032483\n",
            "8999 0.0072742663\n",
            "9499 0.0054949825\n",
            "9999 0.00783928\n",
            "10499 0.010721574\n",
            "10999 0.0072897924\n",
            "11499 0.008039358\n",
            "11999 0.005920907\n",
            "12499 0.0062364563\n",
            "12999 0.0043166806\n",
            "13499 0.0037766579\n",
            "13999 0.0049056956\n",
            "14499 0.0049356623\n",
            "14999 0.004917792\n",
            "15499 0.004502083\n",
            "15999 0.0045606075\n",
            "16499 0.0051525976\n",
            "16999 0.0035542285\n",
            "17499 0.004582064\n",
            "17999 0.004368516\n",
            "18499 0.0039048584\n",
            "18999 0.0041929362\n",
            "19499 0.0052653076\n",
            "19999 0.0041607423\n",
            "20499 0.0038153687\n",
            "20999 0.0032301648\n",
            "21499 0.00384568\n",
            "21999 0.0034206028\n",
            "22499 0.0045954506\n",
            "22999 0.0039034765\n",
            "23499 0.0042015165\n",
            "23999 0.003900434\n",
            "24499 0.0043615797\n",
            "24999 0.003726912\n",
            "25499 0.003570927\n",
            "25999 0.004892994\n",
            "26499 0.00482392\n",
            "26999 0.0029818115\n",
            "27499 0.0029902444\n",
            "27999 0.003111011\n",
            "28499 0.003394891\n",
            "28999 0.0031108027\n",
            "29499 0.003081153\n",
            "29999 0.0029467177\n",
            "30499 0.0029842588\n",
            "30999 0.003401784\n",
            "31499 0.0029459428\n",
            "31999 0.0036289236\n",
            "32499 0.0035273435\n",
            "32999 0.0035856015\n",
            "33499 0.0036093497\n",
            "33999 0.0035161586\n",
            "34499 0.0038450821\n",
            "34999 0.0024034744\n",
            "35499 0.0031220869\n",
            "35999 0.0035113704\n",
            "36499 0.0031091291\n",
            "36999 0.0028633445\n",
            "37499 0.00333607\n",
            "37999 0.0031974108\n",
            "38499 0.003772957\n",
            "38999 0.004842807\n",
            "39499 0.003267464\n",
            "39999 0.0031058204\n",
            "CPU times: user 19min 5s, sys: 1min 42s, total: 20min 47s\n",
            "Wall time: 15min 37s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bbiZWZf_5HRB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "demo_dataset = make_dataset(snapshots, hparams, Dataset.VALIDATION, repeat=False, evaluation=True)\n",
        "\n",
        "tensors = demo_dataset.make_one_shot_iterator().get_next()\n",
        "tensors['predictions'] = predict(tensors['inputs'], hparams)\n",
        "\n",
        "array_list = []\n",
        "while True:\n",
        "  try:\n",
        "    array_list.append(sess.run(tensors))\n",
        "  except tf.errors.OutOfRangeError:\n",
        "    break\n",
        "arrays = {k: np.concatenate([d[k] for d in array_list])\n",
        "          for k in array_list[0]}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "La9fTelO5D3h",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ds = xarray.Dataset({\n",
        "    'inputs': (('sample', 'x'), arrays['inputs']),\n",
        "    'labels': (('sample', 'x', 'offset'), arrays['labels']),\n",
        "    'nn_predictions': (('sample', 'x', 'offset'), arrays['predictions']),\n",
        "    'poly_predictions': (('sample', 'x', 'accuracy_order', 'offset'),\n",
        "                          np.stack([arrays['baseline_1'],arrays['baseline_3'], arrays['baseline_5']], axis=-2)),\n",
        "}, coords={'accuracy_order': [1, 3, 5]})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b6tGWf5k-3Lg",
        "colab_type": "code",
        "outputId": "a38cec8d-731d-47b7-ebf1-c99597fcadfc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "ds"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<pre>&lt;xarray.Dataset&gt;\n",
              "Dimensions:           (accuracy_order: 3, offset: 7, sample: 2000, x: 64)\n",
              "Coordinates:\n",
              "  * accuracy_order    (accuracy_order) int64 1 3 5\n",
              "Dimensions without coordinates: offset, sample, x\n",
              "Data variables:\n",
              "    inputs            (sample, x) float32 0.97113955 0.8941418 ... -0.08285153\n",
              "    labels            (sample, x, offset) float32 1.0328023 ... -0.10640238\n",
              "    nn_predictions    (sample, x, offset) float32 1.0325156 ... -0.106427915\n",
              "    poly_predictions  (sample, x, accuracy_order, offset) float32 1.0318207 ... -0.10643323</pre>"
            ],
            "text/plain": [
              "<xarray.Dataset>\n",
              "Dimensions:           (accuracy_order: 3, offset: 7, sample: 2000, x: 64)\n",
              "Coordinates:\n",
              "  * accuracy_order    (accuracy_order) int64 1 3 5\n",
              "Dimensions without coordinates: offset, sample, x\n",
              "Data variables:\n",
              "    inputs            (sample, x) float32 0.97113955 0.8941418 ... -0.08285153\n",
              "    labels            (sample, x, offset) float32 1.0328023 ... -0.10640238\n",
              "    nn_predictions    (sample, x, offset) float32 1.0325156 ... -0.106427915\n",
              "    poly_predictions  (sample, x, accuracy_order, offset) float32 1.0318207 ... -0.10643323"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eloz95cIACXU",
        "colab_type": "text"
      },
      "source": [
        "## Examine results from the trained model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N16nApb4_kR0",
        "colab_type": "code",
        "outputId": "81f851f4-a67d-4005-d2c5-2b9f2dd9173c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "!gsutil cp gs://data-driven-discretization-public/reconstruction/burgers_results_8x.nc ."
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Copying gs://data-driven-discretization-public/reconstruction/burgers_results_8x.nc...\n",
            "/ [1 files][ 17.6 MiB/ 17.6 MiB]                                                \n",
            "Operation completed over 1 objects/17.6 MiB.                                     \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gTxdSx-eCADS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "hparams = pde.training.create_hparams(\n",
        "    equation='burgers',\n",
        "    conservative=False,\n",
        "    coefficient_grid_min_size=6,\n",
        "    resample_factor=8,\n",
        "    equation_kwargs=json.dumps(dict(num_points=512)),\n",
        "    eval_interval=500,\n",
        "    learning_stops=[20000, 40000],\n",
        "    learning_rates=[3e-3, 3e-4],\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iVyC_AST59EG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ds = xarray.open_dataset('burgers_results_8x.nc').load()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QXzLK5kpAVhb",
        "colab_type": "code",
        "outputId": "8ce6aab9-9060-425b-e83e-4beed5fe517f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "ds"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<pre>&lt;xarray.Dataset&gt;\n",
              "Dimensions:           (accuracy_order: 3, offset: 7, sample: 2000, x: 64)\n",
              "Coordinates:\n",
              "  * accuracy_order    (accuracy_order) int32 1 3 5\n",
              "Dimensions without coordinates: offset, sample, x\n",
              "Data variables:\n",
              "    labels            (sample, x, offset) float32 1.0328023 ... -0.10640238\n",
              "    nn_predictions    (sample, x, offset) float32 1.0326428 ... -0.10649262\n",
              "    poly_predictions  (sample, x, accuracy_order, offset) float32 1.0318207 ... -0.10643323\n",
              "    inputs            (sample, x) float32 0.97113955 0.8941418 ... -0.08285153</pre>"
            ],
            "text/plain": [
              "<xarray.Dataset>\n",
              "Dimensions:           (accuracy_order: 3, offset: 7, sample: 2000, x: 64)\n",
              "Coordinates:\n",
              "  * accuracy_order    (accuracy_order) int32 1 3 5\n",
              "Dimensions without coordinates: offset, sample, x\n",
              "Data variables:\n",
              "    labels            (sample, x, offset) float32 1.0328023 ... -0.10640238\n",
              "    nn_predictions    (sample, x, offset) float32 1.0326428 ... -0.10649262\n",
              "    poly_predictions  (sample, x, accuracy_order, offset) float32 1.0318207 ... -0.10643323\n",
              "    inputs            (sample, x) float32 0.97113955 0.8941418 ... -0.08285153"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2oWgVtdyQrwQ",
        "colab_type": "text"
      },
      "source": [
        "## Figures"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h-nesjtNOtSK",
        "colab_type": "text"
      },
      "source": [
        "Overall histogram of absolut error"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ou_StDBJfXxL",
        "colab_type": "code",
        "outputId": "393d632b-acc4-489d-db0a-2a8b60a13a8b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "plt.hist(abs(ds.labels - ds.poly_predictions.sel(accuracy_order=3)).data.ravel(),\n",
        "         bins=np.geomspace(1e-6, 2, num=51), alpha=0.5, label='3rd order');\n",
        "plt.hist(abs(ds.labels - ds.nn_predictions).data.ravel(),\n",
        "         bins=np.geomspace(1e-6, 2, num=51), alpha=0.5, label='neural net');\n",
        "plt.xscale('log')\n",
        "plt.legend()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f6fe7e24c50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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NAe5Poz17Ab+KiD9IWgvcLelK4BXgotR+GXA20Ah8AFwBEBE7JP0QWJva/SAidqTpGcCd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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-nUc9nZUOx2e",
        "colab_type": "text"
      },
      "source": [
        "Some full examples -- we are much better than third-order polynomial interpolation!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ea6dM2gsoY5U",
        "colab_type": "code",
        "outputId": "5712e4e1-9ced-4ef1-e221-d876b72d9aaa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 863
        }
      },
      "source": [
        "example_id = 0\n",
        "fig, axes = plt.subplots(3, 1, figsize=(10, 15))\n",
        "x = np.arange(512) * 2 * np.pi / 512\n",
        "colors = seaborn.color_palette(n_colors=3)\n",
        "\n",
        "for ax, example_id in zip(axes.ravel(), [0, 10, 20]):\n",
        "  ax.scatter(x[hparams.resample_factor-1::hparams.resample_factor],\n",
        "             ds.inputs.data[example_id],\n",
        "             marker='s',  color=colors[0])\n",
        "  ax.plot(x, stack_reconstruction(ds.inputs.data, ds.labels.data)[example_id],\n",
        "          label='exact', color=colors[0])\n",
        "  ax.plot(x, stack_reconstruction(ds.inputs.data, ds.poly_predictions.sel(accuracy_order=3).data)[example_id],\n",
        "          label='baseline', color=colors[1])\n",
        "  ax.plot(x, stack_reconstruction(ds.inputs.data, ds.nn_predictions.data)[example_id],\n",
        "          label='predictions', linestyle='--', color=colors[2])\n"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1Z85rrHXaGOsdG7c2XaqbY5ocpIhudtRH5QY+cRp4w27OHnj84R+fOwwAW9UG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\nGEmyJYQQQggRRpJsCSGEEEKE0f8DTS5IQNg5ZloAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x1080 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lH1WdCcyPG8t",
        "colab_type": "text"
      },
      "source": [
        "Create Figure 1 from the paper:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T9N_KFVjPThw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "disc = xarray.Dataset()\n",
        "disc['nn_error'] = abs(ds.nn_predictions - ds.labels).mean('offset')\n",
        "disc['poly_error'] = abs(ds.poly_predictions.sel(accuracy_order=3) - ds.labels).mean('offset')\n",
        "\n",
        "# https://en.wikipedia.org/wiki/Curvature#Curvature_of_the_graph_of_a_function\n",
        "use_slope = 0\n",
        "dx = 2*np.pi/512\n",
        "y_xx = ds.labels.diff('offset', 2) / dx ** 2\n",
        "y_x = 0.5 * (ds.labels.diff('offset').sel(offset=slice(None, -1)) + ds.labels.diff('offset').sel(offset=slice(1, None))) / dx\n",
        "disc['bin_curvature'] = (abs(y_xx) / (1 + use_slope * y_x ** 2) ** (3/2)).max('offset')\n",
        "\n",
        "y = stack_reconstruction(ds.inputs.data, ds.labels.data).astype(np.float64)\n",
        "y_xx = (np.roll(y, -1, axis=-1) - 2 * y + np.roll(y, 1, axis=-1)) / dx ** 2\n",
        "y_x = (0.5 * np.roll(y, -1, axis=-1) - 0.5 * np.roll(y, 1, axis=-1)) / dx\n",
        "curvature = abs(y_xx) / (1 + use_slope * y_x ** 2) ** (3/2)\n",
        "resample_factor = 8\n",
        "curvature = np.stack([curvature[:, offset-1::resample_factor]\n",
        "                      for offset in range(1, resample_factor)], axis=-1)\n",
        "disc['curvature'] = ds.labels.copy(data=curvature)\n",
        "\n",
        "disc['nearest_curvature'] = 10 ** np.log10(disc.bin_curvature).round(1)\n",
        "\n",
        "df = disc.to_dataframe().reset_index()\n",
        "curvature_count = (df.groupby('nearest_curvature').count())['sample']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "61D_maBQPGFl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "# TODO(shoyer): upstream this into Seaborn\n",
        "\n",
        "class CustomLinePlotter(seaborn.relational._LinePlotter):\n",
        "    def aggregate(self, vals, grouper, units=None):\n",
        "        \"\"\"Compute an estimate and confidence interval using grouper.\"\"\"\n",
        "        func = self.estimator\n",
        "        ci = self.ci\n",
        "        n_boot = self.n_boot\n",
        "\n",
        "        # Define a \"null\" CI for when we only have one value\n",
        "        null_ci = pd.Series(index=[\"low\", \"high\"], dtype=np.float)\n",
        "\n",
        "        # Group and get the aggregation estimate\n",
        "        grouped = vals.groupby(grouper, sort=self.sort)\n",
        "        est = grouped.agg(func)\n",
        "\n",
        "        lower = grouped.quantile(1 - ci)\n",
        "        upper = grouped.quantile(ci)\n",
        "        cis = pd.DataFrame(np.c_[lower, upper],\n",
        "                            index=est.index,\n",
        "                            columns=[\"low\", \"high\"]).stack()\n",
        "\n",
        "        # Unpack the CIs into \"wide\" format for plotting\n",
        "        if cis.notnull().any():\n",
        "            cis = cis.unstack().reindex(est.index)\n",
        "        else:\n",
        "            cis = None\n",
        "\n",
        "        return est.index, est, cis\n",
        "\n",
        "def custom_lineplot(x=None, y=None, hue=None, size=None, style=None, data=None,\n",
        "             palette=None, hue_order=None, hue_norm=None,\n",
        "             sizes=None, size_order=None, size_norm=None,\n",
        "             dashes=True, markers=None, style_order=None,\n",
        "             units=None, estimator=\"mean\", ci=95, n_boot=1000,\n",
        "             sort=True, err_style=\"band\", err_kws=None,\n",
        "             legend=\"brief\", ax=None, **kwargs):\n",
        "\n",
        "    p = CustomLinePlotter(\n",
        "        x=x, y=y, hue=hue, size=size, style=style, data=data,\n",
        "        palette=palette, hue_order=hue_order, hue_norm=hue_norm,\n",
        "        sizes=sizes, size_order=size_order, size_norm=size_norm,\n",
        "        dashes=dashes, markers=markers, style_order=style_order,\n",
        "        units=units, estimator=estimator, ci=ci, n_boot=n_boot,\n",
        "        sort=sort, err_style=err_style, err_kws=err_kws, legend=legend,\n",
        "    )\n",
        "\n",
        "    if ax is None:\n",
        "        ax = plt.gca()\n",
        "\n",
        "    p.plot(ax, kwargs)\n",
        "    return ax\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TiAkIskcO9N-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "outputId": "e1b5aa0e-565b-4f3a-e800-266b8d52b2a4"
      },
      "source": [
        "seaborn.set_context(\"notebook\", font_scale=12/11)\n",
        "fig = plt.figure(figsize=(2*3.42, 2*2))\n",
        "\n",
        "# LEFT\n",
        "example_id = 0\n",
        "ax = fig.subplots(1, 1, gridspec_kw=dict(bottom=0.11, top=0.985, left=0.095, right=0.58))\n",
        "x = np.arange(512) * 2 * np.pi / 512\n",
        "colors = ['C0', 'C1', 'C2']\n",
        "ax.scatter(x[hparams.resample_factor-1::hparams.resample_factor],\n",
        "           ds.inputs.data[example_id],\n",
        "           marker='D', label='Known points', color=colors[0])\n",
        "ax.plot(x, stack_reconstruction(ds.inputs.data, ds.labels.data)[example_id],\n",
        "         label='Exact solution',\n",
        "         color=colors[0],\n",
        "         linewidth=3,\n",
        "         )\n",
        "ax.plot(x, stack_reconstruction(ds.inputs.data, ds.poly_predictions.sel(accuracy_order=3).data)[example_id],\n",
        "         label='Polynomial interp.', color=colors[1])\n",
        "ax.plot(x, stack_reconstruction(ds.inputs.data, ds.nn_predictions.data)[example_id],\n",
        "        label='Neural net interp.', linestyle='--', color=colors[-1])\n",
        "ax.legend(frameon=False, loc='lower left')\n",
        "ax.set_xlim(1.13, 1.88)\n",
        "ax.set_ylim(-1.2, 1.2)\n",
        "ax.set_xlabel('$x$', labelpad=1)\n",
        "ax.set_ylabel('$v$', labelpad=-5)\n",
        "seaborn.despine()\n",
        "\n",
        "# TOP RIGHT\n",
        "axes = fig.subplots(1, 2, sharex=False, sharey=True,\n",
        "                    gridspec_kw=dict(bottom=0.65, top=0.945, left=0.58, right=0.92, wspace=0))\n",
        "axes[0].set_aspect('equal')\n",
        "axes[1].set_aspect('equal')\n",
        "\n",
        "bins = np.linspace(-2, 2, num=51)\n",
        "im = axes[0].hist2d(\n",
        "    ds.labels.data.ravel(),\n",
        "    ds.poly_predictions.sel(accuracy_order=3).data.ravel(),\n",
        "    bins=2*[bins], cmin=1, norm=LogNorm(vmin=1, vmax=1e4))\n",
        "im[-1].set_edgecolor('none')\n",
        "im[-1].set_rasterized(True)\n",
        "im[-1].set_zorder(-1)\n",
        "\n",
        "im = axes[1].hist2d(\n",
        "    ds.labels.data.ravel(), \n",
        "    ds.nn_predictions.data.ravel(),\n",
        "    bins=2*[bins], cmin=1, norm=LogNorm(vmin=1, vmax=1e4))\n",
        "im[-1].set_edgecolor('none')\n",
        "im[-1].set_rasterized(True)\n",
        "im[-1].set_zorder(-1)\n",
        "\n",
        "cbaxes = fig.add_axes([0.93, 0.65, 0.01, 0.295]) \n",
        "cb = plt.colorbar(im[3], cax=cbaxes, extendfrac=0.05, extend='max')\n",
        "\n",
        "axes[0].set_title('Polynomial')\n",
        "axes[1].set_title('Neural net')\n",
        "\n",
        "axes[0].set_xticks([-2, 0, 2])\n",
        "axes[0].set_xticklabels(['-2', '0', '2 '])\n",
        "axes[0].get_xaxis().majorTicks[2].label1.set_horizontalalignment('right')\n",
        "\n",
        "axes[1].set_xticks([-2, 0, 2])\n",
        "axes[1].get_xaxis().majorTicks[0].label1.set_horizontalalignment('left')\n",
        "axes[0].set_yticks([-2, 0, 2])\n",
        "\n",
        "axes[0].set_xlabel(r'$v_\\mathrm{exact}$', labelpad=1)\n",
        "axes[1].set_xlabel(r'$v_\\mathrm{exact}$', labelpad=1)\n",
        "axes[0].set_ylabel(r'$v_\\mathrm{predicted}$', labelpad=-3)\n",
        "\n",
        "# BOTTOM RIGHT\n",
        "xmin = 1e-1\n",
        "xmax = 1e4\n",
        "ax = fig.subplots(\n",
        "    1, 1, gridspec_kw=dict(bottom=0.115, top=0.46, left=0.73, right=1))\n",
        "custom_lineplot(x='nearest_curvature', y='poly_error', data=df, ax=ax, color=colors[1], estimator=np.median, ci=0.95)\n",
        "custom_lineplot(x='nearest_curvature', y='nn_error', data=df, ax=ax, color=colors[-1], estimator=np.median, ci=0.95)\n",
        "plt.setp(ax.get_lines()[1], linestyle='--')\n",
        "ax.text(4e2, 2.5e0, 'Polynomial', va='center', ha='center')\n",
        "ax.text(1e3, 2e-4, 'Neural\\nnet', va='center', ha='center')\n",
        "ax.set_xscale('log')\n",
        "ax.set_xlim(xmin, xmax)\n",
        "ax.set_yscale('log')\n",
        "ax.set_yticks([1e-4, 1e-2, 1])\n",
        "ax.set_xlabel(r'Curvature', labelpad=1)\n",
        "ax.set_ylabel('Abs. error', labelpad=1)\n",
        "seaborn.despine(ax=ax)\n",
        "\n",
        "plt.figtext(0, 1, '(a)', ha='left', va='top')\n",
        "plt.figtext(.5, 1, '(b)', ha='left', va='top')\n",
        "plt.figtext(.62, 0.48, '(c)', ha='left', va='top')\n",
        "\n",
        "fig.dpi = 90\n"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/matplotlib/colors.py:1110: RuntimeWarning: invalid value encountered in less_equal\n",
            "  mask |= resdat <= 0\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6V/vH1ASc/Pz8BvX4rrzySjZu3Oi3/EsL0M6eRqNpMa2axtVoNMDWV+Htq5SjN+chmPf3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\ncKVXNOCGp5RERSFFG4ADItsoE2nTvI+fh207DzgC+Imq/jVs+8stnP9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32zhnDnBzC7tO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JOWlnZO7hsYGOh9XZsTm82Gn1/jvJmc2kXD67UHviKNBlIh9L7enYVHQgA2ZhjeYlHEIVxCS7ec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\n",
            "text/plain": [
              "<Figure size 615.6x360 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bPWahAa4O-TZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}